Wafer Map Recognition Method Using Artificial Intelligence AND Computer Device

Abstract

A wafer map recognition method using artificial intelligence includes obtaining wafer maps of a plurality of wafers; performing an unsupervised algorithm on the wafer map of each wafer in the plurality of wafers to generate a feature data set for the corresponding wafer map; and performing a clustering algorithm according to a plurality of feature data sets for the plurality of wafer maps to find a wafer map with a potential defect.

What is claimed is: 
     
         1 . A wafer map recognition method, comprising:
 obtaining wafer maps of a plurality of wafers;   performing an unsupervised algorithm on the wafer map of each wafer in the plurality of wafers to generate a feature data set for the corresponding wafer map; and   performing a clustering algorithm according to a plurality of feature data sets for the plurality of wafer maps to find a wafer map with a potential defect.   
     
     
         2 . The method of  claim 1 , further comprising assessing whether the wafer map with a potential defect actually has a defect based on the pattern of the wafer map. 
     
     
         3 . The method of  claim 2 , further comprising:
 if the wafer map with a potential defect has not a defect based on the pattern of the wafer map, feeding back information according to the pattern on the wafer map to a product monitoring module; and   when receiving a wafer map with the same pattern again, do not perform the unsupervised algorithm on the wafer map with the same pattern.   
     
     
         4 . The method of  claim 2 , further comprising:
 if the wafer map with a potential defect actually has a defect based on the pattern of the wafer map, performing a disposition flow according to the pattern on the wafer map.   
     
     
         5 . The method of  claim 4 , wherein the feature data set is a multi-dimensional vector, and the element in the multi-dimensional vector is used for indicating a probability that the wafer map has a certain feature. 
     
     
         6 . The method of  claim 1 , wherein the wafer map contains at least part of an image of the wafer. 
     
     
         7 . The method of  claim 5 , wherein performing a clustering algorithm according to a plurality of feature data sets for the plurality of wafer maps comprises projecting the plurality of multi-dimensional vectors for the plurality of wafers to a plurality of points in a multi-dimensional space, wherein a multi-dimensional vector of each wafer is projected to one point in the multi-dimensional space. 
     
     
         8 . The method of  claim 1 , wherein the wafer map with the potential defect is an outlier generated by performing the clustering algorithm. 
     
     
         9 . The method of  claim 1 , wherein the unsupervised algorithm contains vision transformation, pooling and/or dimension reduction. 
     
     
         10 . The method of  claim 1 , wherein the clustering algorithm is a DBSCAN (density-based spatial clustering of applications with noise) algorithm. 
     
     
         11 . The method of  claim 1 , wherein the wafer maps of a plurality of wafers are generated according to measurement data obtained by measuring the plurality of wafers. 
     
     
         12 . The method of  claim 11 , further comprising:
 performing normalization operations, smoothing, and/or missing value imputation to the measurement data before generating the plurality of wafer maps.   
     
     
         13 . A computer device, comprising:
 a processor; and   a memory storing instructions, wherein the instructions are performed by the processor to perform:
 obtaining wafer maps of a plurality of wafers; 
 performing an unsupervised algorithm on the wafer map of each wafer in the plurality of wafers to generate a feature data set for the corresponding wafer map; and 
 performing a clustering algorithm according to a plurality of feature data sets for the plurality of wafer maps to find a wafer map with a potential defect. 
   
     
     
         14 . The device of  claim 13 , wherein the feature data set is a multi-dimensional vector, and the element in the multi-dimensional vector is used for indicating a probability that the wafer map has a certain feature. 
     
     
         15 . The device of  claim 14 , wherein performing a clustering algorithm according to a plurality of feature data sets for the plurality of wafer maps comprises projecting the plurality of multi-dimensional vectors for the plurality of wafers to a plurality of points in a multi-dimensional space, wherein a multi-dimensional vector of each wafer is projected to one point in the multi-dimensional space. 
     
     
         16 . The device of  claim 13 , wherein the wafer map with the potential defect is an outlier generated by performing the clustering algorithm.